In appearance-based methods for object detection and/or recognition, images indicative of the objects of interest are typically limited to constant and/or slowly varying illumination conditions. Detection of objects within an image is typically compromised by sudden changes in illumination.
Object detection in video surveillance systems, for example, is typically achieved with background subtraction or by using temporal differences. Most change detection methods employ adaptive background models. These methods work well when there are no illumination changes, or only slow illumination changes. Unfortunately, when there is a sudden illumination change, such as might be caused by turning on a light, these methods generally fail.
Dynamic illumination changes with varying degrees of change have also been handled with a multi-scale temporal statistical model. However, the choice of the statistical model is not motivated from a careful analysis of the sensor and illumination parameters. The camera employed generally has an adaptive gain and normally responds in a nonlinear fashion to sudden illumination effects. Thus, even areas in the image not directly influenced by external light sources may change significantly.
A physically motivated approach to change detection has also been presented. In that approach, a homomorphic filter was used to facilitate change detection under illumination changes. Another approach known as the wallflower system maintains several background models, which each represent different illumination conditions. When too many pixels are detected as changing, it checks all the background models, and the background that produces the least foreground pixels is chosen to be the current background.
A similar method was also used where, instead of maintaining more than one frame-level background model, the background was allowed to adapt to lighting changes very quickly when the growth rate of the object pixels was radical. In yet another approach, it has been shown that a Hidden Markov Model can be used to describe global state changes.
What is needed is a robust approach to scene change or object detection that is suitable for use in the presence of sudden illumination changes.